Google data in bridge equation models for German GDP - Thomas B. Götz; Thomas A. Knetsch (Deutsche Bundesbank, 2017)
There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a version of which usually belongs to the suite of forecasting models at central banks. We show how to integrate these big data information, emphasizing the appeal of the underlying model in this respect. As the choice of which Google search terms to add to which equation is crucial - for the forecasting performance itself as well as for the economic consistency of the implied relationships - we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo-real time out-of-sample forecast performance for GDP, various GDP components and monthly activity indicators. We find that there are indeed sizeable gains possible from using Google search data, whereby partial least squares and LASSO appear most promising. Also, the forecast potential of Google search terms vis-avis survey indicators seems th have increased in recent years, suggesting that their scope in this field of application could increase in the future.
Keywords: C22; C32; C53; 330; Big Data; Bridge Equation Models; Forecasting; Principal Components Analysis; Partial Least Squares; LASSO; Boosting